Menafsirkan hasil prediksi dari model deteksi objek gambar
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Setelah meminta prediksi, Vertex AI akan menampilkan hasil berdasarkan
tujuan model Anda. Respons prediksi deteksi objek gambar AutoML menampilkan semua objek yang ditemukan dalam gambar. Setiap objek yang ditemukan memiliki anotasi (label
dan kotak pembatas yang dinormalisasi) dengan skor keyakinan yang sesuai. Kotak
pembatas ditulis sebagai:
"bboxes": [
[xMin, xMax, yMin, yMax],
...]
Dengan xMin, xMax adalah nilai x minimum dan maksimum serta
yMin, yMax masing-masing adalah nilai y minimum dan maksimum.
Contoh output prediksi batch
Respons prediksi deteksi objek gambar AutoML batch disimpan sebagai
file Baris JSON di bucket Cloud Storage. Setiap baris file Garis
JSON
berisi semua objek yang ditemukan dalam satu file gambar. Setiap objek yang ditemukan memiliki
anotasi (label dan kotak pembatas yang dinormalisasi) dengan skor keyakinan
yang sesuai.
Penting: Kotak pembatas ditentukan sebagai:
"bboxes": [
[xMin, xMax, yMin, yMax],
...]
Dengan xMin dan xMax adalah nilai x minimum dan maksimum, serta
yMin dan yMax masing-masing adalah nilai y minimum dan maksimum.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-02 UTC."],[],[],null,["# Interpret prediction results from image object detection models\n\nAfter requesting a prediction, Vertex AI returns results based on your model's objective. AutoML image object detection prediction responses return all objects found in an image. Each found object has an annotation (label and normalized bounding box) with a corresponding confidence score. The bounding box is written as:\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\n`\n\"bboxes\": [\n[xMin, xMax, yMin, yMax],\n...]\n`\nWhere `xMin, xMax` are the minimum and maximum x values and `\nyMin, yMax` are the minimum and maximum y values respectively.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n#### Example batch prediction output\n\nBatch AutoML image object detection prediction responses are stored as\nJSON Lines files in Cloud Storage buckets. Each line of the JSON Lines\nfile\ncontains all objects found in a single image file. Each found object has\nan annotation (label and normalized bounding box) with a corresponding\nconfidence score.\n| **Note: Zero coordinate values omitted.** When the API detects a coordinate (\"x\" or \"y\") value of 0, ***that coordinate is omitted in the\n| JSON response*** . Thus, a response with a bounding poly around the entire image would be \n| **\\[{},{\"x\": 1,\"y\": 1}\\]** . For more information, see [Method: projects.locations.models.predict](https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/predict#boundingpoly).\n\n\n| **Note**: The following JSON Lines example includes line breaks for\n| readability. In your JSON Lines files, line breaks are included only after each\n| each JSON object.\n\n\u003cbr /\u003e\n\n\n\u003cbr /\u003e\n\n**Important:** Bounding boxes are specified as:\n\n\n`\n\"bboxes\": [\n[xMin, xMax, yMin, yMax],\n...]\n`\nWhere `xMin` and `xMax` are the minimum and maximum x values and `\nyMin` and `yMax` are the minimum and maximum y values respectively.\n\n\u003cbr /\u003e\n\n```\n{\n \"instance\": {\"content\": \"gs://bucket/image.jpg\", \"mimeType\": \"image/jpeg\"},\n \"prediction\": {\n \"ids\": [1, 2],\n \"displayNames\": [\"cat\", \"dog\"],\n \"bboxes\": [\n [0.1, 0.2, 0.3, 0.4],\n [0.2, 0.3, 0.4, 0.5]\n ],\n \"confidences\": [0.7, 0.5]\n }\n}\n```"]]